code: def test_getitem(
      is_training: bool, is_evaluation: bool, length: int, mixup: bool
  ) -> None:
      opts = argparse.Namespace()
      setattr(opts, "dataset.speech_commands_v2.mixup", mixup)
      setattr(opts, "dataset.root_train", "/tmp/train")
      setattr(opts, "dataset.root_val", "/tmp/val")
      setattr(opts, "dataset.root_test", "/tmp/test")
      setattr(
          opts, "audio_augmentation.set_fixed_length.length", length
      )
      setattr(opts, "audio_augmentation.noise.enable", False)
      setattr(opts, "audio_augmentation.roll.enable", False)
  
      dataset = SpeechCommandsv2Dataset(
          opts, is_training=is_training, is_evaluation=is_evaluation
      )
  
      value1 = dataset[(None, None, 0)]
      value2 = dataset[(None, None, 1)]
  
      assert torch.any(
          value1["samples"]["audio"] != value2["samples"]["audio"]
      )
      assert value1["samples"]["audio"].shape == (1, length)
  
      # Make sure the elements can be collated.
      collate_functions.pytorch_default_collate_fn(
          [value1, value2], opts
      )
  
file_path: ml-cvnets/tests/data/datasets/audio_classification/test_speech_commands_v2.py
imports: import argparse
  from typing import Tuple
  from unittest import mock
  import pytest
  import torch
  from torch import Tensor
  from data.collate_fns import collate_functions
  from data.datasets.audio_classification.speech_commands_v2 import SpeechCommandsv2Dataset
label_desc: Use image node_id 3 for calling a global function with example usage: test_getitem(is_training, is_evaluation, length, mixup) without return types